| Abstract |
Assessing species' extinction risk is vital to setting conservation priorities. However, assessment endeavours like the IUCN Red List of Threatened Species have significant gaps in taxonomic coverage. Automated assessment (AA) methods are gaining popularity to fill these gaps. Choices made in developing, using, and reporting AA methods could hinder successful adoption or lead to poor allocation of conservation resources. We explored how choice of data-cleaning, taxonomic group, training sample, and automation method affected performance of threat status predictions for plant species. We used occurrences from GBIF to generate assessments for species in three taxonomic groups using six different occurrence-based AA methods. We measured each method's performance and coverage following increasingly stringent occurrence cleaning. Automatically-cleaned data from GBIF yielded comparable performance to occurrence records cleaned manually by experts. However, all types of data-cleaning limited the coverage of automated assessments. Overall, machine-learning-based methods performed well across taxa, even with minimal data-cleaning. Results suggest a machine-learning-based method on minimally-cleaned data offers the best compromise between performance and species coverage. However, optimal data-cleaning, training sample, and automation methods depend on the study group, intended applications and expertise.
Article impact statement: Evidence-based guidelines for automated conservation methods will make their use easier and more reliable for conservation decisions.
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